[论文解读] An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning

Nabin K. Mishra, M. Emre Celebi|arXiv (Cornell University)|Jan 28, 2016

Cutaneous Melanoma Detection and Management被引用 107

一句话总结

对皮肤镜图像中自动黑色素瘤检测的综述，概述病变分割、特征提取及机器学习分类，以及向临床应用的挑战。

ABSTRACT

The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology has allowed accumulation of large collections of dermoscopy images of melanomas and benign lesions validated by histopathology. The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow not only earlier detection of melanoma, but also reduction of the large number of needless and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, widespread implementation must await further technical progress in accuracy and reproducibility. In this paper, we provide an overview of computerized detection of melanoma in dermoscopy images. First, we discuss the various aspects of lesion segmentation. Then, we provide a brief overview of clinical feature segmentation. Finally, we discuss the classification stage where machine learning algorithms are applied to the attributes generated from the segmented features to predict the existence of melanoma.

研究动机与目标

• 推动在早期黑色素瘤检测中使用皮肤镜，并讨论其对临床诊断的影响。
• 概述分析皮肤镜图像的图像处理步骤，特别是病变分割。
• 评述临床特征如何被分割并转化为机器学习可处理的属性。
• 讨论应用于分割特征以预测黑色素瘤的机器学习分类器。
• 强调当前的局限性以及对准确性、可重复性和更广泛应用的需求。

提出的方法

• 描述皮肤镜在实现早期黑色素瘤检测中的作用。
• 概述皮肤镜图像中的病变分割方法。
• 概述皮肤镜的临床特征分割。
• 讨论从分割特征到基于机器学习的分类的流程。
• 回顾用于从提取的特征预测黑色素瘤的机器学习算法。
• 解决与准确性、可重复性和临床应用相关的挑战。

实验结果

研究问题

• RQ1 皮肤镜病变的关键分割方法是什么及其效果如何？
• RQ2 从皮肤镜图像中提取哪些临床特征，以及它们如何转化为机器学习属性？
• RQ3 哪些机器学习分类器适用于从皮肤镜提取的特征预测黑色素瘤？
• RQ4 在准确性和可重复性方面，广泛临床应用存在哪些障碍？
• RQ5 如何进一步改进皮肤镜图像中的自动黑色素瘤检测，以减少不必要的活检？

主要发现

• 皮肤镜在早期黑色素瘤检测和诊断能力方面取得了进展。
• 图像处理的进步使自动区分黑色素瘤与良性仿制病例成为可能。
• 当前系统在初步试验中显示出潜力，但需要在准确性和可重复性方面改进以实现广泛应用。
• 开发全自动检测是可行的，但尚未准备好实现普遍的临床应用。
• 从分割到分类的流程是皮肤镜图像中黑色素瘤预测的核心。
• 通过可靠的自动化需要减少不必要且成本高昂的活检。